“Expanding the Verbal Horizons: Fun Techniques for Boosting the Power of Mammoth Language Models”
“A new way to increase the capabilities of large language models”
“Inspired by the promise of large language models (LLMs), senior post-doc at MIT, Dan Garrette, notes that LLMs, like Google Translate or OpenAI’s GPT-3, have been fine-tuned up to this point with training on a heap of web-scraped text data. But the hard limit of how much you can improve these models lies, surprisingly, not in the quantity of data, but in the computational and financial cost of training on such colossal datasets.”
Revolutionary, isn’t it? Who would have thought that the limitations of these brainchild AI models aren’t restricted by the amount of data you can throw at them, but by the gigantic computational and financial muscle needed to train them? Talk about a fascinating twist of events!
According to the report, the folks at MIT and the University of Massachusetts Amherst have been playing around with a novel concept – “prompt engineering.” As the name subtly suggests, it’s the art of tweaking and refining the instruction phrasing to achieve a more accurate result, rather than loading the AI with mind-numbing amounts of data.
Sounds simple, right? Like telling your smart home device to be “less literal and more human” in the hopes it’ll finally understand you want the temperature set at a cozy 72 degrees and NOT 100 degrees because you said you’re freezing. The cheek!
Now, you must be thinking, “but this isn’t an earth-shattering revelation!” Well, you’re right. “Prompt engineering” isn’t totally new, and it has been dabbling around the edges of AI research. However, the method hasn’t been optimized until now. To wrap your head around it, let’s take a real-world example, the English proficiency test TOEFL.
When participants are asked to summarize a piece of text, they are often given explicit instructions on how to do it. This concept should apply equally to our beloved language models. If you instruct them appropriately, you can cut down the overwhelming amount of data they need to gulp down to produce accurate text. Phrasing the instructions just right can bring a smarter, sharper AI model with more efficient use of resources. Cue the collective sigh of relief from data centres worldwide!
But hey, the clever folks at MIT want to go one step ahead! They are not just looking at crafting better prompts but are also planning to develop a new transfer-learning paradigm to navigate the explosive growth and complex, constantly evolving landscape of AI technology.
And to that we say, “bring it on!” Here’s to pushing the boundaries and questioning the status quo because let’s face it, in the world of AI, there’s always room for improvement.
So, as we keep upgrading your tech with “prompt engineering” prowess, rest easy knowing that the future looks quite promising. And remember, in the world of artificial intelligence, the devil is in the details or in this case, the prompt!